Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity

• URL: http://arxiv.org/abs/2104.13061v1
• Date: Tue, 27 Apr 2021 09:19:36 GMT
• Title: Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity
• Authors: Mathias P. M. Parisot, Balazs Pejo and Dayana Spagnuelo
• Abstract summary: Property Inference Attacks aim to infer from a given model properties about the training dataset seemingly unrelated to the model's primary goal. This paper investigates the influence of the target model's complexity on the accuracy of this type of attack. Our findings reveal that the risk of a privacy breach is present independently of the target model's complexity.
• Score: 1.2891210250935143
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning models' goal is to make correct predictions for specific tasks by learning important properties and patterns from data. By doing so, there is a chance that the model learns properties that are unrelated to its primary task. Property Inference Attacks exploit this and aim to infer from a given model (\ie the target model) properties about the training dataset seemingly unrelated to the model's primary goal. If the training data is sensitive, such an attack could lead to privacy leakage. This paper investigates the influence of the target model's complexity on the accuracy of this type of attack, focusing on convolutional neural network classifiers. We perform attacks on models that are trained on facial images to predict whether someone's mouth is open. Our attacks' goal is to infer whether the training dataset is balanced gender-wise. Our findings reveal that the risk of a privacy breach is present independently of the target model's complexity: for all studied architectures, the attack's accuracy is clearly over the baseline. We discuss the implication of the property inference on personal data in the light of Data Protection Regulations and Guidelines.

Related papers

• Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models [112.48136829374741]
  In this paper, we unveil a new vulnerability: the privacy backdoor attack. When a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. Our findings highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• When Machine Learning Models Leak: An Exploration of Synthetic Training Data [0.0]
  We investigate an attack on a machine learning model that predicts whether a person or household will relocate in the next two years. The attack assumes that the attacker can query the model to obtain predictions and that the marginal distribution of the data on which the model was trained is publicly available. We explore how replacing the original data with synthetic data when training the model impacts how successfully the attacker can infer sensitive attributes.
  arXiv  Detail & Related papers  (2023-10-12T23:47:22Z)
• Property inference attack; Graph neural networks; Privacy attacks and defense; Trustworthy machine learning [5.598383724295497]
  Machine learning models are vulnerable to privacy attacks that leak information about the training data. In this work, we focus on a particular type of privacy attacks named property inference attack (PIA) We consider Graph Neural Networks (GNNs) as the target model, and distribution of particular groups of nodes and links in the training graph as the target property.
  arXiv  Detail & Related papers  (2022-09-02T14:59:37Z)
• SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles [50.90773979394264]
  This paper studies a model that protects the privacy of individuals' sensitive information while also allowing it to learn non-discriminatory predictors. A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
  arXiv  Detail & Related papers  (2022-04-11T14:42:54Z)
• Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets [53.866927712193416]
  We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak private details belonging to other parties. Our attacks are effective across membership inference, attribute inference, and data extraction. Our results cast doubts on the relevance of cryptographic privacy guarantees in multiparty protocols for machine learning.
  arXiv  Detail & Related papers  (2022-03-31T18:06:28Z)
• Variational Model Inversion Attacks [26.613251410498755]
  In model inversion attacks, a malicious user attempts to recover the private dataset used to train a supervised neural network. A successful model inversion attack should generate realistic and diverse samples that accurately describe each of the classes in the private dataset. In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.
  arXiv  Detail & Related papers  (2022-01-26T07:39:13Z)
• Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models [22.569705869469814]
  We focus on model inversion attacks where the adversary knows non-sensitive attributes about records in the training data. We devise a novel confidence score-based model inversion attribute inference attack that significantly outperforms the state-of-the-art. We also extend our attacks to the scenario where some of the other (non-sensitive) attributes of a target record are unknown to the adversary.
  arXiv  Detail & Related papers  (2022-01-23T21:27:20Z)
• Enhanced Membership Inference Attacks against Machine Learning Models [9.26208227402571]
  Membership inference attacks are used to quantify the private information that a model leaks about the individual data points in its training set. We derive new attack algorithms that can achieve a high AUC score while also highlighting the different factors that affect their performance. Our algorithms capture a very precise approximation of privacy loss in models, and can be used as a tool to perform an accurate and informed estimation of privacy risk in machine learning models.
  arXiv  Detail & Related papers  (2021-11-18T13:31:22Z)
• ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models [64.03398193325572]
  Inference attacks against Machine Learning (ML) models allow adversaries to learn about training data, model parameters, etc. We concentrate on four attacks - namely, membership inference, model inversion, attribute inference, and model stealing. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models.
  arXiv  Detail & Related papers  (2021-02-04T11:35:13Z)
• Knowledge-Enriched Distributional Model Inversion Attacks [49.43828150561947]
  Model inversion (MI) attacks are aimed at reconstructing training data from model parameters. We present a novel inversion-specific GAN that can better distill knowledge useful for performing attacks on private models from public data. Our experiments show that the combination of these techniques can significantly boost the success rate of the state-of-the-art MI attacks by 150%.
  arXiv  Detail & Related papers  (2020-10-08T16:20:48Z)
• Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations [81.82518920087175]
  Adversarial attacking aims to fool deep neural networks with adversarial examples. We propose a reinforcement learning based attack model, which can learn from attack history and launch attacks more efficiently.
  arXiv  Detail & Related papers  (2020-09-19T09:12:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.